R: Compute path radiance based on the dark object method
calcPathRadDOS
R Documentation
Compute path radiance based on the dark object method
Description
Compute an estimated path radiance for all sensor bands, which can then be
used to roughly correct the radiance values for atmospheric scattering. Path
radiance estimation is based on a dark object method.
A Satellite object or the value (scaled count) of a dark object in
bnbr (e.g. minimum raw count of selected raster bnbr). If x is
a Satellite object, the value is computed using calcDODN.
model
Model to be used to correct for 1% scattering (DOS2, DOS4; must
be the same as used by calcAtmosCorr).
esun_method
If x is a Satellite object, name of the method to be used
to compute esun using one of calcTOAIrradRadRef ("RadRef"),
calcTOAIrradTable ("Table") or calcTOAIrradModel
("Model")
use_cpp
Logical. If TRUE, C++ functionality (via Rcpp)
is enabled, which leads to a considerable reduction of both computation time
and memory usage.
bnbr
Band number for which DNmin is valid.
band_wls
Band wavelengths to be corrected; data.frame with min
(max) in first (second) column, see details.
radm
Multiplicative coefficient for radiance transformation (i.e.
slope).
rada
Additive coefficient for radiance transformation (i.e. offset).
szen
Sun zenith angle.
esun
Actual (i.e. non-normalized) TOA solar irradiance, e.g. returned
by calcTOAIrradRadRef, calcTOAIrradTable or
calcTOAIrradModel.
scat_coef
Scattering coefficient; defaults to -4.0.
dos_adjust
Assumed reflection for dark object adjustment; defaults to 0.01.
Details
If x is a Satellite object, the minimum raw count value (x) is computed using
calcDODN. If the TOA solar irradiance is not part of the
Satellite object's metadata, it is computed using
calcTOAIrradRadRef, calcTOAIrradTable or
calcTOAIrradModel.
The dark object subtraction approach is based on an approximation
of the atmospheric path radiance (i.e. upwelling radiation which is
scattered into the sensors field of view, aka haze) using the reflectance of a
dark object (i.e. reflectance ~1%).
Chavez (1988) proposed a method which uses the dark object reflectance
in one band to predict the corresponding path radiances in all other
band_wls. This is done using a relative radiance model which depends on
the wavelength and overall atmospheric optical thickness (which is estimated
based on the dark object's DN value). This has the advantage that the path
radiance is actually correlated across different sensor band_wls and
not computed individually for each band using independent dark objects. He
proposed a relative radiance model which follows a wavelength dependent
scattering that ranges from a power of -4 over -2, -1, -0.7 to -0.5 for very
clear over clear, moderate, hazy to very hazy conditions. The relative
factors are computed individually for each 1/1000 wavelength within each band
range and subsequently averaged over the band as proposed by Goslee (2011).
The atmospheric transmittance towards the sensor (Tv) is approximated by
1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992)
The atmospheric transmittance from the sun (Tz) is approximated by the
cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh
scattering (DOS4, Moran et al. 1992).
The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996)
or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992).
Equations for the path radiance are taken from Song et al. (2001).
For some sensors, the band wavelengths are already included. See
lutInfo()[grepl("_BANDS", names(lutInfo()$META))] if your sensor is
included. To retrieve a sensor, use lutInfo()$<Sensor ID>_BANDS.
Value
Satellite object with path radiance for each band in the metadata
(W m-2 micrometer-1)
Vector object with path radiance values for each band
(W m-2 micrometer-1)
References
Chavez Jr PS (1988) An improved dark-object subtraction technique
for atmospheric scattering correction of multispectral data. Remote Sensing
of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at
http://www.sciencedirect.com/science/article/pii/0034425788900193.
Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat
Package. Journal of Statistical Software,43/4, 1-25, available online at
http://www.jstatsoft.org/v43/i04/.
Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evlauation of simplified
procedures for rretrieval of land surface reflectane factors from satellite
sensor output.Remote Sensing of Environment 41/2-3, 169-184,
doi:10.1016/0034-4257(92)90076-V, available online at
http://www.sciencedirect.com/science/article/pii/003442579290076V.
Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification
and Change Detection Using Landsat TM Data: When and How to Correct
Atmospheric Effects? Remote Sensing of Environment 75/2,
doi:10.1016/S0034-4257(00)00169-3, available online at
http://www.sciencedirect.com/science/article/pii/S0034425700001693
If you refer to Sawyer and Stephen 2014, please note that eq. 5 is wrong.
See Also
This function is used by calcAtmosCorr to
compute the path radiance.